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- W4317987766 abstract "High-resolution images are being generated due to the rapid development of image sensors. Various image processing algorithms require images of reduced sizes, given the computational constraints. Hence, preprocessing the images to reduce their size is crucial for many applications. This letter proposes a novel approach to compress images using quantum computing. A comparative study on different standard data encoding techniques used in quantum computing is undertaken. We propose four quantum compression techniques by extending the unitary operations of amplitude embedding for compressing images. The proposed methods provide exponential scaling as amplitude embedding is used, where <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink><tex-math notation=LaTeX>$2^{n}$</tex-math></inline-formula> classical data values are encoded into <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink><tex-math notation=LaTeX>$n$</tex-math></inline-formula> qubits. Compression performance, visual evaluation, and objective evaluation are carried out to assess the proposed compression techniques. Our experimental results show that the crucial patterns in images are retained in the compressed images even at 75% compression. The compressed images can be used for postprocessing tasks using classical or quantum computing algorithms." @default.
- W4317987766 created "2023-01-25" @default.
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- W4317987766 date "2023-02-01" @default.
- W4317987766 modified "2023-10-14" @default.
- W4317987766 title "Quantum Approach to Image Data Encoding and Compression" @default.
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- W4317987766 doi "https://doi.org/10.1109/lsens.2023.3239749" @default.
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